Japanese--Russian--English News Commentary Parallel Data

Introduction

This repository contains manually curated parallel sentences for Japanese--Russian, Japanese--English, and Russian--English language pairs in news domain.

Contents

The Japanese--Russian is one of the most distant language pairs and has only limited quantity of parallel data to train machine translation (MT) systems. To promote the research on low-resource MT, we have curated parallel sentences, which can be used as development and test data, through the following procedure:

1. Downloaded from OPUS News Commentary data for Japanese--Russian with 586 sentence pairs and Japanese--English with 637 sentence pairs.
2. The above Japanese--Russian and Japanese--English data share many lines in the Japanese side. Therefore, we first compiled a Russian--Japanese--English tri-text data.
3. From each line, we identified corresponding parts across languages, and split off unaligned parts into a new line.
4. As a result, we obtained 1,654 lines of data comprising trilingual, bilingual, and monolingual segments (mainly sentences).
5. For the sake of comparability, we randomly chose 600 trilingual sentences to create a test set, and concatenated the rest of them and bilingual sentences to form development sets.

Distribution of tri-texts

Ru	Ja	En	#sent	Test	Dev
✓	✓	✓	913	600	313
✓	✓	-	173	-	173
-	✓	✓	276	-	276
✓	-	✓	0	-	-
✓	-	-	4	-	-
-	✓	-	287	-	-
-	-	✓	1	-	-

Development and test splits (available in this repository)

L1--L2	Development	Test
Japanese--Russian	486	600
Japanese--English	589	600
Russian--English	313	600

Benchmarking

Scoreboard (BLEU-cased)

System description	Resources Used	Ja-to-Ru	Ru-to-Ja
Uni-directional Transformer NMT	(a)	0.70	1.96
Multi-to-multi Transformer NMT involving English	(a)	3.72	8.35
Same but with multi-lingual multi-stage fine-tuning	(a) (b) (c) (d)	7.49	12.10

Data used for above systems are as follows:

(a) Global Voices parallel data retrieved from OPUS (v2015; included in this repository)

(b) ASPEC: Asian Scientific Paper Excerpt Corpus (out-of-domain Japanese--English parallel data)

(c) UN provided for WMT 18 (out-of-domain Russian--English parallel data)

(d) Yandex provided for WMT 18 (out-of-domain Russian--English parallel data)

References

• Aizhan Imankulova, Raj Dabre, Atsushi Fujita, and Kenji Imamura. Exploiting Out-of-Domain Parallel Data through Multilingual Transfer Learning for Low-Resource Neural Machine Translation. In Proceedings of the 17th Machine Translation Summit (MT Summit), Aug., 2019. (to appear). arXiv

Precautions

• National Institute of Information and Communications Technology (henceforth, NICT) has made the database publicly available under the conditions of license specified below.
• NICT bears no responsibility for the contents of the database and assumes no liability for any direct or indirect damage or loss whatsoever that may be incurred as a result of using the database.
• If any copyright infringement or other problems are found in the database, please contact us at atsushi.fujita[at]nict[dot]go[dot]jp. We will review the issue and undertake appropriate measures when needed.

License

No claims of intellectual property are made on the work of preparation of the corpus. See the OPUS and/or CASMACAT for details.

Acknowledgments

The dataset has been developed as a part of work at Advanced Translation Technology Laboratory, Advanced Speech Translation Research and Development Promotion Center, National Institute of Information and Communications Technology.